{"id":"https://openalex.org/W7161967918","doi":"https://doi.org/10.48550/arxiv.2605.20461","title":"Understanding Model Behavior in Monocular Polyp Sizing","display_name":"Understanding Model Behavior in Monocular Polyp Sizing","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161967918","doi":"https://doi.org/10.48550/arxiv.2605.20461"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20461","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20461","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136648813","display_name":"Xinqi Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Xinqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070152688","display_name":"Andrea Dunn Beltran","orcid":"https://orcid.org/0009-0009-1399-9670"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beltran, Andrea Dunn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136690718","display_name":"Junmyeong Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Junmyeong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012954579","display_name":"Sarah McGill","orcid":"https://orcid.org/0000-0002-4006-2703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McGill, Sarah K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108610850","display_name":"Marc Niethammer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niethammer, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102635961","display_name":"Roni Sengupta","orcid":"https://orcid.org/0000-0001-5914-3469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sengupta, Roni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9010000228881836,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9010000228881836,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.013700000010430813,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.011300000362098217,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6158999800682068},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5892000198364258},{"id":"https://openalex.org/keywords/sizing","display_name":"Sizing","score":0.512499988079071},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.46790000796318054},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4377000033855438},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.38429999351501465},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.37790000438690186},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.3560999929904938}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65829998254776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6305000185966492},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6158999800682068},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5892000198364258},{"id":"https://openalex.org/C2777767291","wikidata":"https://www.wikidata.org/wiki/Q1080291","display_name":"Sizing","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44940000772476196},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.3028999865055084},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28700000047683716},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20461","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.20461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20461","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.43364009261131287,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"polyp":[1,32,162],"size":[2,33],"stratification":[3],"guides":[4],"surveillance":[5],"decisions,":[6],"with":[7,72,122],"lesions":[8],"larger":[9],"than":[10,76],"5":[11],"mm":[12,36],"typically":[13],"requiring":[14],"closer":[15],"follow-up.":[16],"However,":[17],"monocular":[18],"colonoscopy":[19],"lacks":[20],"a":[21,27],"reliable":[22],"metric":[23,78,135],"reference.":[24],"We":[25,108],"present":[26],"diagnostic":[28],"audit":[29],"of":[30,119],"binary":[31],"classification":[34],"(<=5":[35],"vs.":[37],">5":[38],"mm)":[39],"across":[40],"multiple":[41],"public":[42],"multi-center":[43],"datasets,":[44],"model":[45,62],"families,":[46],"and":[47,52,60,96,102,137,144,156],"patient-stratified":[48],"cross-validation.":[49],"Across":[50],"architectures":[51],"input":[53],"modalities,":[54],"including":[55],"RGB":[56],"appearance,":[57],"relative":[58],"depth,":[59],"photometry,":[61],"performance":[63],"is":[64,167],"moderately":[65],"consistent,":[66],"suggesting":[67],"reliance":[68],"on":[69],"cues":[70],"correlated":[71],"examination":[73],"behavior":[74],"rather":[75],"true":[77],"scales.":[79],"By":[80],"providing":[81],"ground-truth":[82],"scale":[83,94,124,136,152],"at":[84,170],"varying":[85],"granularities,":[86],"we":[87],"quantify":[88],"the":[89],"potential":[90],"improvement":[91],"from":[92],"perfect":[93],"information":[95],"show":[97],"that":[98,111],"current":[99],"depth":[100],"estimation":[101],"global":[103],"calibration":[104],"offer":[105],"limited":[106],"gains.":[107],"further":[109],"demonstrate":[110],"segmentation":[112],"errors":[113],"under":[114,125],"distribution":[115],"shift":[116],"eliminate":[117],"most":[118],"this":[120],"potential,":[121],"oracle":[123,151],"predicted":[126],"masks":[127],"recovering":[128],"only":[129],"baseline":[130],"performance.":[131],"These":[132],"results":[133],"highlight":[134],"mask":[138,157],"robustness":[139],"as":[140,150],"two":[141],"independent":[142],"bottlenecks":[143],"provide":[145],"reusable":[146],"evaluation":[147],"tools":[148],"such":[149],"ladders,":[153],"shortcut":[154],"partitions,":[155],"substitution":[158],"for":[159],"auditing":[160],"future":[161],"sizing":[163],"pipelines.":[164],"Our":[165],"code":[166],"publicly":[168],"accessible":[169],"https://github.com/anaxqx/polyp-sizing-audit.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}